A Novel Association Rule Decision Algorithm Based on Ant Colony Optimization Algorithm for Ball Mill Pulverizing System

Optimization of automatic control is very important in ball mill pulverizing system and a novel association rule decision algorithm based on ant colony optimization algorithm (ACO) is proposed for improving the control performance. The proposed algorithm, based on ACO, formulates the association rule decision problem as an optimization problem. In the algorithm, a new pheromone matrix is defined on the construction of the problem, and an effective heuristic values assignment approach, which is used with the knowledge of controlled plant, is proposed. The fitness function is established on the system control quality, such as the overshoot, the settling time and the steady state error. We performed the experiments on a mined association rules base of ball mill pulverizing system. Simulation results verify that the proposed algorithm can find the best association rule to optimize the system control performance.